工业物联网环境下软件缺陷预测技术的发展与应用综述

doi:10.11896/jsjkx.250200052

摘要/Abstract

摘要： 在工业物联网(Industrial Internet of Things,IIoT) 环境中,海量的软件代码数据的生成迫切需要通过先进的软件缺陷预测(Software Defect Prediction,SDP)技术进行有效分析。这些技术不仅能够迅速定位异常情况,还可以全面调查潜在问题,因为即使是微小的偏差也可能导致项目代码的崩溃。文中系统综述了2018－2025年间发表的61篇相关文献,突出展示了IIoT中SDP所面临的主要挑战和最新进展。从多个视角深入探讨了SDP的相关技术,包括统计方法、机器学习技术和模型导向的方法等。未来的研究应优先关注复杂异构环境中缺陷模式的动态变化,解决数据稀缺和标注成本高昂的问题,同时平衡实时性与资源限制之间的矛盾。此外,需要增强模型的可解释性和用户的认知理解,以提升系统的可理解性和操作的鲁棒性。还对IIoT中相关的现有数据集进行了系统分析,为该关键领域的进一步研究奠定了坚实基础。

关键词: 工业物联网, 软件缺陷预测, 模型导向

Abstract: In the context of IIoT,the generation of vast amounts of software code data necessitates effective analysis through advanced SDP techniques.These techniques not only enable the rapid identification of anomalies but also facilitate comprehensive investigations into potential issues,as even minor deviations can lead to significant code failures.This paper systematically reviews over 61 relevant articles published between 2018 and 2025,highlighting the primary challenges and recent advancements in SDP within IIoT.Various perspectives on SDP technologies are explored,including statistical methods,machine learning approaches,and model-oriented techniques.Future research should prioritize the dynamics of defect patterns in complex heterogeneous environments,address the challenges of data scarcity and high labeling costs,and balance the trade-off between real-time processing and resource constraints.Additionally,the interpretability of models and user cognitive understanding must be enhanced to improve system comprehensibility and operational robustness.A comprehensive analysis of existing datasets related to IIoT is also presented,laying a solid foundation for further research in this critical area.

Key words: Industrial Internet of Things, Software defect prediction, Model-oriented

中图分类号:

TP311

邓涛, 邓烨. 工业物联网环境下软件缺陷预测技术的发展与应用综述[J]. 计算机科学, 2025, 52(11A): 250200052-11. https://doi.org/10.11896/jsjkx.250200052

DENG Tao, DENG Ye. Review of Development and Application of Software Defect Prediction Techniques in IndustrialInternet of Things Environment[J]. Computer Science, 2025, 52(11A): 250200052-11. https://doi.org/10.11896/jsjkx.250200052

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